3.2. Matched-filter detection

This tutorial will cover using both the match-filter objects, and using the internal functions within match-filter. The match-filter objects are designed to simplify meta-data handling allowing for shorter code with fewer mistakes and therefore more consistent results.

3.2.1. Match-filter objects

The match-filter module contains five objects:

The Tribe object is a container for multiple Template objects. Templates contain the waveforms of the template alongside the metadata used to generate the template. Both Templates and Tribes can be written to disk as tar archives containing the waveform data in miniseed format, event catalogues associated with the Templates (if provided) in quakeml format and meta-data in a csv file. This archives can be read back in or transferred between machines.

The Detection, Family and Party objects are heirachical, a single Detection object describes a single event detection, and contains information regarding how the detection was made, what time it was made at alongside other useful information, it does not store the Template object used for the detection, but does store a reference to the name of the Template. Family objects are containers for multiple Detections made using a single Template (name chosen to match the literature). These objects do contain the Template used for the detections, and as such can be used to re-create the list of detections is necessary. Party objects are containers for multiple Family objects. All objects in the detection heirachy have read and write methods - we recommend writing to tar archives (default) for Party and Family objects, as this will store all metadata used in detection, which should allow for straightforward reproduction of results.

3.2.2. Template creation

Templates have a construct method which accesses the functions in template_gen. Template.construct only has access to methods that work on individual events, and not catalogs; for that use the Tribe.construct method. For example, we can use the from_sac method to make a Template from a series of SAC files associated with a single event:

import glob
from eqcorrscan.core.match_filter import Template
sac_files = glob.glob('eqcorrscan/tests/test_data/SAC/2014p611252')
# sac_files is now a list of all the SAC files for event id:2014p611252
template = Template().construct(
     method='from_sac', name='test', lowcut=2.0, highcut=8.0,
     samp_rate=20.0, filt_order=4, prepick=0.1, swin='all',
     length=2.0, sac_files=sac_files)

3.2.3. Tribe creation

As eluded to above, Template.construct only works for individual events, to make a lot of templates we have to use the Tribe.construct method. The syntax is similar, but we don’t specify names - templates are named according to their start-time, but you can rename them later if you wish:

from eqcorrscan.core.match_filter import Tribe
from obspy.clients.fdsn import Client

client = Client('NCEDC')
catalog = client.get_events(eventid='72572665', includearrivals=True)
# To speed the example we have a catalog of one event, but you can have
# more, we are also only using the first five picks, again to speed the
# example.
catalog[0].picks = catalog[0].picks[0:5]
tribe = Tribe().construct(
     method='from_client', catalog=catalog, client_id='NCEDC', lowcut=2.0,
     highcut=8.0,  samp_rate=20.0, filt_order=4, length=6.0, prepick=0.1,
     swin='all', process_len=3600, all_horiz=True)

3.2.4. Matched-filter detection using a Tribe

Both Tribe and Template objects have detect methods. These methods call the main match_filter function. They can be given an un-processed stream and will complete the appropriate processing using the same processing values stored in the Template objects. Because Tribe objects can contain Templates with a range of processing values, this work is completed in groups for groups of Templates with the same processing values. The Tribe object also has a client_detect method which will download the appropriate data. Both detect and client_detect methods return Party objects.

For example, we can use the Tribe we created above to detect through a day of data by running the following:

from obspy import UTCDateTime

party, stream = tribe.client_detect(
     client=client, starttime=UTCDateTime(2016, 1, 2),
     endtime=UTCDateTime(2016, 1, 3), threshold=8, threshold_type='MAD',
     trig_int=6, plotvar=False, return_stream=True)

3.2.5. Lag-calc using a Party

Because parties contain Detection and Template information they can be used to generate re-picked catalogues using lag-calc:

repicked_catalog = party.lag_calc(
     stream, pre_processed=False, shift_len=0.2, min_cc=0.4)

By using the above examples you can go from a standard catalog available from data centers, to a matched-filter detected and cross-correlation repicked catalog in a handful of lines.

3.2.6. Simple example - match-filter.match-filter

This example does not work out of the box, you will have to have your own templates and data, and set things up for this. However, in principle matched-filtering can be as simple as:

from eqcorrscan.core import match_filter
from eqcorrscan.utils import pre_processing
from obspy import read

# Read in and process the daylong data
st = read('continuous_data')
# Use the same filtering and sampling parameters as your template!
st = pre_processing.dayproc(st, lowcut=2, highcut=10, filt_order=4,
# Read in the templates
templates = []
template_names = ['template_1', 'template_2']
for template_file in template_names:
detections = match_filter.match_filter(
     template_names=template_names, template_list=templates, st=st,
     threshold=8, threshold_type='MAD', trig_int=6, plotvar=False, cores=4)

This will create a list of detections, which are of class detection. You can write out the detections to a csv (colon separated) using the detection.write method, set append=True to write all the detections to one file. Beware though, if this is set and the file already exists, it will just add on to the old file.

for detection in detections:
     detection.write('my_first_detections.csv', append=True)

3.2.7. Memory limitations and what to do about it

You may (if you are running large numbers of templates, long data durations, or using a machine with small memory) run in to errors to do with memory consumption. The most obvious symptom of this is your computer freezing because it has allocated all of its RAM, or declaring that it cannot allocate memory. Because EQcorrscan computes correlations in parallel for multiple templates for the same data period, it will generate a large number of correlation vectors. At start-up, EQcorrscan will try to assign the memory it needs (although it then requires a little more later to do the summation across channels), so you might find that it fills your memory very early - this is just to increase efficiency and ensure that the memory is available when needed.

To get around memory limitations you can:

  • Reduce the number of templates you run in parallel at once - for example you can make groups of a number of templates and run that group in parallel, before running the next group in parallel. This is not much less efficient, unless you have a machine with more CPU cores than your group-size.
  • Reduce the length of data you are correlating at any one time. The default is to use day-long files, but there is nothing stopping you using shorter waveform durations.
  • Reduce the number of channels in templates to only those that you need. Note, EQcorrscan will generate vectors of zeros for templates that are missing a channel that is present in other templates, again for processing efficiency, if not memory efficiency.
  • Reduce your sampling rate. Obviously this needs to be at-least twice as large as your upper frequency filter, but much above this is wasted data.

As an example of this: we run 100, 5-channel templates sampled at 20 Hz through day-long data on a 128GB RAM machine without issue, however, running 200 templates is too much memory.

3.2.8. The three threshold parameters

The match-filter routine has three key threshold parameters:

  • threshold_type can either be MAD, abs or av_chan_corr. MAD stands for Median Absolute Deviation and is the most commonly used detection statistic in matched-filter studies. abs is the absolute cross-channel correlation sum, note that if you have different numbers of channels in your templates then this threshold metric probably isn’t for you. av_chan_corr sets a threshold in the cross-channel correlation sum based on av_chan_corr x number of channels.
  • threshold is the value used for the above metric.
  • trig_int is the minimum interval in seconds for a detection using the same template. If there are multiple detections within this window for a single template then EQcorrscan will only give the best one (that exceeds the threshold the most).

3.2.9. Advanced example - match-filter-match-filter

In this section we will outline using the templates generated in the first tutorial to scan for similar earthquakes within a day of data. This small example does not truly exploit the parallel operations within this package however, so you would be encouraged to think about where parallel operations occur (hint, the code can run one template per CPU), and why there are instance andsplits flags in the other scripts in the github repository (hint, if you have heaps of memory and CPUs you can do some brute force day parallelisation!).

The main processing flow is outlined in the figure below, note the main speedups in this process are achieved by running multiple templates at once, however this increases memory usage. If memory is a problem there are flags (mem_issue) in the match_filter.py source that can be turned on - the codes will then write temporary files, which is slower, but can allow for more data crunching at once, your trade-off, your call.

Simple tutorial to demonstrate some of the basic capabilities of the EQcorrscan
matched-filter detection routine.  This builds on the template generation
tutorial and uses those templates.  If you haven't run that tutorial script
then you will need to before you can run this script.

import glob

from multiprocessing import cpu_count
from obspy.clients.fdsn import Client
from obspy import UTCDateTime, Stream, read

from eqcorrscan.utils import pre_processing
from eqcorrscan.utils import plotting
from eqcorrscan.core import match_filter

def run_tutorial(plot=False):
    """Main function to run the tutorial dataset."""
    # First we want to load our templates
    template_names = glob.glob('tutorial_template_*.ms')

    if len(template_names) == 0:
        raise IOError('Template files not found, have you run the template ' +
                      'creation tutorial?')

    templates = [read(template_name) for template_name in template_names]

    # Work out what stations we have and get the data for them
    stations = []
    for template in templates:
        for tr in template:
            stations.append((tr.stats.station, tr.stats.channel))
    # Get a unique list of stations
    stations = list(set(stations))

    # We will loop through the data chunks at a time, these chunks can be any
    # size, in general we have used 1 day as our standard, but this can be
    # as short as five minutes (for MAD thresholds) or shorter for other
    # threshold metrics. However the chunk size should be the same as your
    # template process_len.

    # You should test different parameters!!!
    start_time = UTCDateTime(2016, 1, 4)
    end_time = UTCDateTime(2016, 1, 5)
    process_len = 3600
    chunks = []
    chunk_start = start_time
    while chunk_start < end_time:
        chunk_end = chunk_start + process_len
        if chunk_end > end_time:
            chunk_end = end_time
        chunks.append((chunk_start, chunk_end))
        chunk_start += process_len

    unique_detections = []
    detections = []

    # Set up a client to access the GeoNet database
    client = Client("GEONET")

    # Note that these chunks do not rely on each other, and could be paralleled
    # on multiple nodes of a distributed cluster, see the SLURM tutorial for
    # an example of this.
    for t1, t2 in chunks:
        # Generate the bulk information to query the GeoNet database
        bulk_info = []
        for station in stations:
            bulk_info.append(('NZ', station[0], '*',
                              station[1][0] + 'H' + station[1][-1], t1, t2))

        # Note this will take a little while.
        print('Downloading seismic data, this may take a while')
        st = client.get_waveforms_bulk(bulk_info)
        # Merge the stream, it will be downloaded in chunks

        # Set how many cores we want to parallel across, we will set this
        # to four as this is the number of templates, if your machine has
        # fewer than four cores/CPUs the multiprocessing will wait until there
        # is a free core.
        # Setting this to be higher than the number of templates will have no
        # increase in speed as only detections for each template are computed
        # in parallel.  It may also slow your processing by using more memory
        # than needed, to the extent that swap may be filled.
        if cpu_count() < 4:
            ncores = cpu_count()
            ncores = 4

        # Pre-process the data to set frequency band and sampling rate
        # Note that this is, and MUST BE the same as the parameters used for
        # the template creation.
        print('Processing the seismic data')
        st = pre_processing.shortproc(st, lowcut=2.0, highcut=9.0,
                                      filt_order=4, samp_rate=20.0,
                                      debug=2, num_cores=ncores, starttime=t1,
        # Convert from list to stream
        st = Stream(st)

        # Now we can conduct the matched-filter detection
        detections += match_filter.match_filter(
            template_names=template_names, template_list=templates,
            st=st, threshold=8.0, threshold_type='MAD', trig_int=6.0,
            plotvar=plot, plotdir='.', cores=ncores, debug=1,

    # Now lets try and work out how many unique events we have just to compare
    # with the GeoNet catalog of 20 events on this day in this sequence
    for master in detections:
        keep = True
        for slave in detections:
            if not master == slave and\
               abs(master.detect_time - slave.detect_time) <= 1.0:
                # If the events are within 1s of each other then test which
                # was the 'best' match, strongest detection
                if not master.detect_val > slave.detect_val:
                    keep = False
                    print('Removed detection at %s with cccsum %s'
                          % (master.detect_time, master.detect_val))
                    print('Keeping detection at %s with cccsum %s'
                          % (slave.detect_time, slave.detect_val))
        if keep:

    print('We made a total of ' + str(len(unique_detections)) + ' detections')

    for detection in unique_detections:
        print('Detection at :' + str(detection.detect_time) +
              ' for template ' + detection.template_name +
              ' with a cross-correlation sum of: ' +
        # We can plot these too
        if plot:
            stplot = st.copy()
            template = templates[template_names.index(detection.template_name)]
            lags = sorted([tr.stats.starttime for tr in template])
            maxlag = lags[-1] - lags[0]
            stplot.trim(starttime=detection.detect_time - 10,
                        endtime=detection.detect_time + maxlag + 10)
            plotting.detection_multiplot(stplot, template,
    return unique_detections

if __name__ == '__main__':

3.2.10. SLURM example

When the authors of EQcorrscan work on large projects, we use grid computers with the SLURM (Simple Linux Utility for Resource Management) job scheduler installed. To facilitate ease of setup, what follows is an example of how we run this.

#SBATCH -J MatchTest
#SBATCH -A ##########
#SBATCH --time=12:00:00
#SBATCH --mem=7G
#SBATCH --nodes=1
#SBATCH --output=matchout_%a.txt
#SBATCH --error=matcherr_%a.txt
#SBATCH --cpus-per-task=16
#SBATCH --array=0-49

# Load the required modules here.
module load OpenCV/2.4.9-intel-2015a
module load ObsPy/0.10.3rc1-intel-2015a-Python-2.7.9
module load joblib/0.8.4-intel-2015a-Python-2.7.9

# Run your python script using srun
srun python2.7 LFEsearch.py --splits 50 --instance $SLURM_ARRAY_TASK_ID

Where we use a script (LFEsearch.py) that accepts splits and instance flags, this section of the script is as follows:

if len(sys.argv) == 2:
    if flag == '--debug':
    elif flag == '--debug-prep':
        raise ValueError("I don't recognise the argument, I only know --debug and --debug-prep")
elif len(sys.argv) == 5:
    # Arguments to allow the code to be run in multiple instances
    for i in xrange(len(args)):
        if args[i] == '--instance':
            print 'I will run this for instance '+str(instance)
        elif args[i] == '--splits':
            print 'I will divide the days into '+str(splits)+' chunks'

elif not len(sys.argv) == 1:
    raise ValueError("I only take one argument, no arguments, or two flags with arguments")

The full script is not included in EQcorrscan, but is available on request.